This invention relates generally to the field of geophysical prospecting. More particularly, the invention is a method of mapping seismic attributes, such as seismic facies, using a neural network.
Seismic facies analysis is an important step in the interpretation of seismic data for reservoir characterization. Seismic facies interpretations play a significant roll in initial basin exploration, prospect evaluation, reservoir characterization, and ultimately, field development. A seismic facies is a stratigraphic unit or region that has a characteristic reflection pattern distinguishable from those of other areas. Regions of differing seismic facies are usually delineated using descriptive terms that reflect large-scale seismic patterns such as reflection amplitude, continuity, and internal configuration of reflectors bounded by stratigraphic horizons.
The application and scale of seismic facies analysis varies significantly, from basin wide applications to detailed reservoir characterization. On a basin-wide scale, reconnaissance seismic facies analysis has been applied in the study of hydrocarbon systems to broadly identify regions of source, reservoir, and seal-prone regions. These regions are usually identified on the basis of their reflection geometry as well as amplitude strength and continuity. Regionally high-amplitude, semi-continuous reflectors are often used to identify potential hydrocarbon-bearing reservoirs, such as deep-water channels, while low-amplitude continuous to semi-continuous regions can be used to identify seal-prone units.
Seismic facies analysis can also be applied within a single reservoir to help constrain a detailed physical-property characterization. In these local-scale applications, definitions of continuity and amplitude generally do not have strict definitions, and are based on rock property calibration or environment of deposition interpretations. Assuming a relationship between seismic character and physical properties can be demonstrated, seismic facies volumes can then be used to predict rock property distribution and condition geologic models.
The standard technology used for seismic facies analysis and mapping is a manual process where the seismic interpreter makes visual decisions about the character of the seismic reflection data within an interval of interest and plots these on a map. Seismic facies are then used for a variety of purposes, but primarily to interpret the distribution of lithofacies and rock properties. A skilled interpreter""s perception, intuition, and experience contribute significantly to the success of seismic facies studies. However, these same strengths can also cause seismic facies analysis to be a subjective, time consuming, and often laborious task. Several related techniques have been used in the oil industry to automate and enhance the interpretation of seismic facies from seismic data.
R. J. Matlock and G. T. Asimakopoulos, xe2x80x9cCan Seismic Stratigraphy Problems be Solved Using Automated Pattern Analysis and Recognition?xe2x80x9d, The Leading Edge, Geophys Explor, Vol. 5, no. 9, pp.51-55, 1986 lay out a conceptual framework for training of an algorithm, and thus automation, of the seismic interpretation process. However, these authors do not demonstrate any working prototype or describe any specifics of the possible attributes or classification algorithms.
R. Vintner, K. Mosegaard, et al., xe2x80x9cSeismic Texture Classification: A Computer-Aided Approach to Stratigraphic Analysisxe2x80x9d, SEG International Exposition and 65th Annual Meeting, paper SL1.4, Oct. 8-13, 1995 and R. Vintner, K. Mosegaard, I. Abatzis, C. Anderson, V. O. Vejbaek, and P. H. Nielson, xe2x80x9c3D Seismic Texture Classificationxe2x80x9d, Society of Petroleum Engineers 35482, 1996, discuss textural analysis of seismic data as well as classification of textural attributes using a version of principal-component analysis and probability distributions. These publications, while using textural analysis methods on seismic data, do not take advantage of probabilistic neural networks or the dynamic use of probability values to optimize the classification. These methods also do not utilize an interactive training scheme and the textural analysis is not dip-steered. The process of guiding a calculation by the stratigraphic layering defined by the dip of the seismic reflectors is called dip-steering.
D. Gao, xe2x80x9cThe First-Order and the Second-Order Seismic Textures: Implications for quantitative Seismic Interpretation and Hydrocarbon Explorationxe2x80x9d, 1999, describes the use of standard textural analysis to produce seismic textural attributes that quantify reflection strength, continuity, and geometry. This abstract does not, however, describe methods of classification of textural attributes. Specifically, Gao, 1999, does not use a probabilistic neural network nor interactive interpreter training of the neural network. Additionally, the textural analysis is not dip-steered.
Turhan Taner, in combination with Rock Solid Images and the Consortium for Computation and Interpretive Use of Seismic Attributes, employs a method in which various seismic attributes are used to interactively train a neural network. However, textural attributes are not used and the network employed is a fully-connected back-propagation neural network, rather than a probabilistic neural network.
P. Meldahl, R. Heggland, P. F. M. de Groot, and A. H. Brill, xe2x80x9cThe Chimney Cube, an Example of Semi-Automated Detection of Seismic Objects by Directive Attributes and Neural Networks: Part I; Methodologyxe2x80x9d, xe2x80x9cThe Chimney Cube, an Example of Semi-Automated Detection of Seismic Objects by Directive Attributes and Neural Networks: Part II; Interpretationxe2x80x9d, and British Patent with International Publication No. WO 00/16125, xe2x80x9cMethod of Seismic Signal Processingxe2x80x9d use seismic attributes to interactively train a neural network and produce a facies volume. However, in the training and production of the chimney cube, only one class of item, instead of multiple classes, is focussed on and classified at a time. Accordingly, only two final output nodes are used in the neural network architecture. A probability cube is computed and then, as a post-processing phase, on-off thresholds are drawn to decide if the object is of the class of interest or not. A complex Wigner-Radon transformation scheme is used for dip-steering the seismic attributes. The attributes are manually chosen for individual classes.
Elf Acquitaine, xe2x80x9cAutomatic Seismic Pattern Recognitionxe2x80x9d, FR 2738920 19970321 and EP 808467 19971126, describe a seismic trace-based method for seismic pattern recognition. Each seismic trace within a user-defined interval is decomposed into a user-defined number of empirical-orthogonal functions. These derived functions are then classified using a neural network based classification algorithm, rather than interpreter-trained textural analysis.
Thus, there exists a need to generate, in a computationally efficient manner, a process that enables the rapid, objective classification of seismic data so that it can be exploited in the seismic facies mapping process. This process must also mimic the process employed by and results obtained manually by the seismic interpreter.
The present invention in one of its embodiments is a method for producing a seismic attribute classification volume corresponding to a seismic data volume obtained from and corresponding to a subterranean region, comprising the steps of:
(a) using the seismic data to calculate values of at least one selected seismic attribute at points throughout said region;
(b) selecting at least one cross-section from each attribute data volume;
(c) constructing a plurality of polygons on the selected cross sections, and making an initial classification of the attribute within each polygon, said polygons being chosen to be collectively representative of the range of attribute values in the respective data volumes;
(d) constructing a probabilistic neural network using the attribute classifications within the polygons to train the network;
(e) using the neural network to produce an attribute classification volume for a portion of the subterranean region;
(f) repeating steps (c) through (e) until the classifications for the portion of the region are considered satisfactory; and
(g) using the constructed probabilistic neural network to produce an attribute classification volume for the entire subterranean region.
In another embodiment, the constructed probabilistic neural network can be used to generate confidence values which can then be used to optimize the retraining of the neural network in the iterative procedure outlined above.
In other embodiments, the present inventive method is applied to map, or produce classification volumes for, quantities or parameters not derived from seismic data, including non-petroleum applications.